69 results found
Rasmussen DA, Volz EM, Koelle K, 2014, Phylodynamic Inference for Structured Epidemiological Models, PLOS COMPUTATIONAL BIOLOGY, Vol: 10, ISSN: 1553-734X
Romero-Severson EO, Meadors GD, Volz EM, 2014, A Generating Function Approach to HIV Transmission with Dynamic Contact Rates, MATHEMATICAL MODELLING OF NATURAL PHENOMENA, Vol: 9, Pages: 121-135, ISSN: 0973-5348
Romero-Severson EO, Meadors GD, Volz EM, 2014, Erratum: A generating function approach to HIV transmission with dynamic contact rates (Mathematical Modelling of Natural Phenomena), Mathematical Modelling of Natural Phenomena, Vol: 9, Pages: 178-181, ISSN: 0973-5348
Volz EM, Ionides E, Romero-Severson EO, et al., 2013, HIV-1 Transmission during Early Infection in Men Who Have Sex with Men: A Phylodynamic Analysis, PLOS MEDICINE, Vol: 10, ISSN: 1549-1277
Volz EM, Frost SDW, 2013, Inferring the Source of Transmission with Phylogenetic Data, PLOS COMPUTATIONAL BIOLOGY, Vol: 9
Miller JC, Volz EM, 2013, Model hierarchies in edge-based compartmental modeling for infectious disease spread, JOURNAL OF MATHEMATICAL BIOLOGY, Vol: 67, Pages: 869-899, ISSN: 0303-6812
Sadasivam RS, Volz EM, Kinney RL, et al., 2013, Share2Quit: Web-Based Peer-Driven Referrals for Smoking Cessation., JMIR Res Protoc, Vol: 2, ISSN: 1929-0748
BACKGROUND: Smoking is the number one preventable cause of death in the United States. Effective Web-assisted tobacco interventions are often underutilized and require new and innovative engagement approaches. Web-based peer-driven chain referrals successfully used outside health care have the potential for increasing the reach of Internet interventions. OBJECTIVE: The objective of our study was to describe the protocol for the development and testing of proactive Web-based chain-referral tools for increasing the access to Decide2Quit.org, a Web-assisted tobacco intervention system. METHODS: We will build and refine proactive chain-referral tools, including email and Facebook referrals. In addition, we will implement respondent-driven sampling (RDS), a controlled chain-referral sampling technique designed to remove inherent biases in chain referrals and obtain a representative sample. We will begin our chain referrals with an initial recruitment of former and current smokers as seeds (initial participants) who will be trained to refer current smokers from their social network using the developed tools. In turn, these newly referred smokers will also be provided the tools to refer other smokers from their social networks. We will model predictors of referral success using sample weights from the RDS to estimate the success of the system in the targeted population. RESULTS: This protocol describes the evaluation of proactive Web-based chain-referral tools, which can be used in tobacco interventions to increase the access to hard-to-reach populations, for promoting smoking cessation. CONCLUSIONS: Share2Quit represents an innovative advancement by capitalizing on naturally occurring technology trends to recruit smokers to Web-assisted tobacco interventions.
Romero-Severson EO, Alam SJ, Volz E, et al., 2013, Acute-Stage Transmission of HIV: Effect of Volatile Contact Rates, EPIDEMIOLOGY, Vol: 24, Pages: 516-521, ISSN: 1044-3983
Frost SDW, Volz EM, 2013, Modelling tree shape and structure in viral phylodynamics, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 368, ISSN: 0962-8436
Volz EM, Koelle K, Bedford T, 2013, Viral Phylodynamics, PLOS COMPUTATIONAL BIOLOGY, Vol: 9, ISSN: 1553-734X
Alam SJ, Zhang X, Romero-Severson EO, et al., 2013, Detectable signals of episodic risk effects on acute HIV transmission: Strategies for analyzing transmission systems using genetic data, EPIDEMICS, Vol: 5, Pages: 44-55, ISSN: 1755-4365
Sadasivam RS, Cutrona SL, Volz E, et al., 2013, Web-based Peer-Driven Chain Referrals for Smoking Cessation, MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2, Vol: 192, Pages: 357-361, ISSN: 0926-9630
Miller JC, Volz EM, 2013, Incorporating Disease and Population Structure into Models of SIR Disease in Contact Networks, PloS one, Vol: 8, Pages: e69162-e69162
Romero-Severson EO, Alam SJ, Volz EM, et al., 2012, Heterogeneity in Number and Type of Sexual Contacts in a Gay Urban Cohort., Stat Commun Infect Dis, Vol: 4, ISSN: 1948-4690
HIV transmission models include heterogeneous individuals with different sexual behaviors including contact rates, mixing patterns, and sexual practices. However, heterogeneity can also exist within individuals over time. In this paper we analyze a two year prospective cohort of 882 gay men with observations at six month intervals focusing on heterogeneity both within and between individuals in sexual contact rates and sexual roles. The total number of sexual contacts made over the course of the study (mean 1.55 per month) are highly variable between individuals (standard deviation 9.82 per month) as expected. At the individual level, contacts were also heterogeneous over time. For a homogeneous count process the variance should scale with the mean; however, at the individual level the variance scaled with the square root of the mean implying the presence of heterogeneity within individuals over time. We also observed a high level of movement between dichotomous sexual roles (insertive/receptive, protected/unprotected, anal/oral, and HIV status of partners). On average periods of exclusively unprotected sexual contacted lasted 16 months. Our results suggest that future HIV models should consider heterogeneities both between and within individuals in sexual contact rates and sexual roles.
Zhang X, Zhong L, Romero-Severson E, et al., 2012, Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection., Stat Commun Infect Dis, Vol: 4, ISSN: 1948-4690
A deterministic compartmental model was explored that relaxed the unrealistic assumption in most HIV transmission models that behaviors of individuals are constant over time. A simple model was formulated to better explain the effects observed. Individuals had a high and a low contact rate and went back and forth between them. This episodic risk behavior interacted with the short period of high transmissibility during acute HIV infection to cause dramatic increases in prevalence as the differences between high and low contact rates increased and as the duration of high risk better matched the duration of acute HIV infection. These same changes caused a considerable increase in the fraction of all transmissions that occurred during acute infection. These strong changes occurred despite a constant total number of contacts and a constant total transmission potential from acute infection. Two phenomena played a strong role in generating these effects. First, people were infected more often during their high contact rate phase and they remained with high contact rates during the highly contagious acute infection stage. Second, when individuals with previously low contact rates moved into an episodic high-risk period, they were more likely to be susceptible and thus provided more high contact rate susceptible individuals who could get infected. These phenomena make test and treat control strategies less effective and could cause some behavioral interventions to increase transmission. Signature effects on genetic patterns between HIV strains could make it possible to determine whether these episodic risk effects are acting in a population.
Bauermeister JA, Zimmerman MA, Johns MM, et al., 2012, Innovative Recruitment Using Online Networks: Lessons Learned From an Online Study of Alcohol and Other Drug Use Utilizing a Web-Based, Respondent-Driven Sampling (webRDS) Strategy, JOURNAL OF STUDIES ON ALCOHOL AND DRUGS, Vol: 73, Pages: 834-838, ISSN: 1937-1888
Volz EM, Koopman JS, Ward MJ, et al., 2012, Simple Epidemiological Dynamics Explain Phylogenetic Clustering of HIV from Patients with Recent Infection, PLOS COMPUTATIONAL BIOLOGY, Vol: 8
Miller JC, Slim AC, Volz EM, 2012, Edge-based compartmental modelling for infectious disease spread, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 9, Pages: 890-906, ISSN: 1742-5689
Volz EM, 2012, Complex Population Dynamics and the Coalescent Under Neutrality, GENETICS, Vol: 190, Pages: 187-U311, ISSN: 0016-6731
Volz EM, Miller JC, Galvani A, et al., 2011, Correction: Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics, PLoS Computational Biology, Vol: 7
Craft ME, Volz E, Packer C, et al., 2011, Disease transmission in territorial populations: the small-world network of Serengeti lions, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 8, Pages: 776-786, ISSN: 1742-5689
Volz EM, Miller JC, Galvani A, et al., 2011, Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics, PLOS COMPUTATIONAL BIOLOGY, Vol: 7
Volz E, Frost SDW, Rothenberg R, et al., 2010, Epidemiological bridging by injection drug use drives an early HIV epidemic, EPIDEMICS, Vol: 2, Pages: 155-164, ISSN: 1755-4365
Frost SDW, Volz EM, 2010, Viral phylodynamics and the search for an 'effective number of infections', PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 365, Pages: 1879-1890, ISSN: 0962-8436
Farber S, Páez A, Volz E, 2010, Topology, dependency tests and estimation bias in network autoregressive models, Advances in Spatial Science, Pages: 29-57
© 2010, Springer-Verlag Berlin Heidelberg. Regression analyses based on spatial datasets often display spatial autocorrelation in the substantive part of the model, or residual pattern in the disturbances. A researcher conducting investigations of a spatial dataset must be able to identify whether this is the case, and if so, what model specification is more appropriate for the data and problem at hand. If autocorrelation is embedded in the dependent variable, the following spatial autoregressive (SAR) model with a spatial lag can be used: (Formula Presented.) On the other hand, when there is residual pattern in the error component of the traditional regression model, the spatial error model (SEM) can be used: (Formula Presented.) In the above equations, W is the spatial weight matrix representing the structure of the spatial relationships between observations, ρ is the spatial dependence parameter, u is a vector of autocorrelated disturbances, and all other terms are the elements commonly found in ordinary linear regression analysis.
Craft ME, Volz E, Packer C, et al., 2009, Distinguishing epidemic waves from disease spillover in a wildlife population, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 276, Pages: 1777-1785, ISSN: 0962-8452
Farber S, Paez A, Volz E, 2009, Topology and Dependency Tests in Spatial and Network Autoregressive Models, GEOGRAPHICAL ANALYSIS, Vol: 41, Pages: 158-180, ISSN: 0016-7363
Volz E, Meyers LA, 2009, Epidemic thresholds in dynamic contact networks, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 6, Pages: 233-241, ISSN: 1742-5689
Abramovitz D, Volz EM, Strathdee SA, et al., 2009, Using Respondent Driven Sampling in a Hidden Population at Risk of HIV Infection: Who do HIV-positive recruiters recruit?, Sexually transmitted diseases, Vol: 36, Pages: 750-750
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